Information processing apparatus, information processing method, and storage medium

ABSTRACT

An information processing apparatus according to the present invention includes: an acceptance unit that accepts a process request to an operation system; a specifying unit that, based on the process request, specifies an operation task to be executed in the operation system; an extraction unit that performs text analysis on the process request and extracts an answer item corresponding to an input item required at execution of the operation task from the process request; and an execution unit that executes the operation task based on the answer item.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/JP2018/040828 filed Nov. 2, 2018, claiming priority based onJapanese Patent Application No. 2017-214979 filed Nov. 7, 2017, thedisclosure of which is incorporated herein in its entirety by reference.

TECHNICAL FIELD

The present invention relates to an information processing apparatus, aninformation processing method, and a storage medium.

BACKGROUND ART

Patent Literature 1 discloses an intelligence automatic assistant systemthat accepts a process request from a user in an interactive form, callsan external service based on input data, and thereby performs a searchprocess or a registration process.

CITATION LIST Patent Literature

-   PTL 1: Japanese Patent Application Publication No. 2013-517566

SUMMARY OF INVENTION Technical Problem

The system disclosed in Patent Literature 1 extracts necessaryinformation from answers based on a predetermined rule. Thus, in orderfor the system to accurately support a difference in meaning accordingto a context, an inconsistency in written forms, or the like for thesame word, it is necessary to predict answer patterns of users toquestionnaires and register a large number of rules in advance.

Accordingly, in view of the above problem, the present invention intendsto provide an information processing apparatus, an informationprocessing method, and a storage medium that can accurately analyze aninput natural language and automatically set input items required inoperating an operation system without requiring pre-registration of alarge number of rules.

Solution to Problem

According to one example aspect of the present invention, provided is aninformation processing apparatus including: an acceptance unit thataccepts a process request to an operation system; a specifying unitthat, based on the process request, specifies an operation task to beexecuted in the operation system; an extraction unit that performs textanalysis on the process request and extracts an answer itemcorresponding to an input item required at execution of the operationtask from the process request; and an execution unit that executes theoperation task based on the answer item.

According to another example aspect of the present invention, providedis an information processing method including: accepting a processrequest to an operation system; based on the process request, specifyingan operation task to be executed in the operation system; performingtext analysis on the process request and extracting an answer itemcorresponding to an input item required at execution of the operationtask from the process request; and executing the operation task based onthe answer item.

According to yet another example aspect of the present invention,provided is a storage medium storing a program that causes a computer toperform: accepting a process request to an operation system; based onthe process request, specifying an operation task to be executed in theoperation system; performing text analysis on the process request andextracting an answer item corresponding to an input item required atexecution of the operation task from the process request; and executingthe operation task based on the answer item.

Advantageous Effects of Invention

According to the present invention, it is possible to provide aninformation processing apparatus, an information processing method, anda storage medium that can accurately analyze an input natural languageand automatically set an input item required in operating an operationsystem without requiring pre-registration of a large number of rules.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating the function of an informationprocessing apparatus according to a first example embodiment.

FIG. 2 is a diagram illustrating a specific example of an entry windowof an operation system in the first example embodiment.

FIG. 3A is a diagram illustrating an example of data items of aninteraction setting storage unit in the first example embodiment.

FIG. 3B is a diagram illustrating an example of data items of theinteraction setting storage unit in the first example embodiment.

FIG. 4 is a diagram illustrating an example of data items of a useranswer storage unit in the first example embodiment.

FIG. 5 is a diagram illustrating an example of data items of an RPAsetting storage unit in the first example embodiment.

FIG. 6 is a block diagram illustrating an example of a hardwareconfiguration of the information processing apparatus according to thefirst example embodiment.

FIG. 7 is a sequence diagram illustrating an example of operationsbetween the information processing apparatus, a user terminal, and theoperation system according to the first example embodiment.

FIG. 8 is a diagram illustrating one example of an interactive window ofthe user terminal in the first example embodiment.

FIG. 9 is a diagram illustrating one example of an interactive window ofthe user terminal in the first example embodiment.

FIG. 10 is a flowchart illustrating one example of an external systemcooperation process of the information processing apparatus according tothe first example embodiment.

FIG. 11 is a block diagram illustrating the function of an informationprocessing apparatus according to a second example embodiment.

FIG. 12 is a flowchart illustrating one example of the informationprocessing apparatus in the second example embodiment.

FIG. 13 is a diagram illustrating one example of an interactive windowof a user terminal in the second example embodiment.

FIG. 14 is a diagram illustrating one example of an interactive windowof the user terminal in the second example embodiment.

FIG. 15 is a flowchart illustrating one example of the informationprocessing apparatus in a third example embodiment.

FIG. 16 is a diagram illustrating one example of an interactive windowof the user terminal in a third example embodiment.

FIG. 17 is a flowchart illustrating one example of the informationprocessing apparatus in a fourth example embodiment.

FIG. 18 is a diagram illustrating one example of an interactive windowof the user terminal in a fourth example embodiment.

FIG. 19 is a block diagram illustrating the function of an informationprocessing apparatus in a fifth example embodiment.

FIG. 20 is a block diagram illustrating the function of an informationprocessing apparatus according to a sixth example embodiment.

DESCRIPTION OF EMBODIMENTS

Example embodiments of the present invention will be described belowwith reference to the drawings. Note that, in the drawings describedbelow, components having the same function or corresponding functionsare labeled with the same references, and repeated description thereofwill be omitted.

First Example Embodiment

FIG. 1 is a block diagram illustrating the function of an informationprocessing apparatus 1 according to a first example embodiment. Theinformation processing apparatus 1 is connected to a user terminal 2, anoperation system 3 including various systems, an external system 4, andan employee management system 5 via a communication network (notillustrated) (for example, a wired LAN, a wireless LAN, the Internet, orthe like). The information processing apparatus 1 is a computer thataccepts a process request from the user terminal 2 in an interactiveform and performs an operation task in the operation system 3. The userterminal 2 is a work station such as a smartphone, a tablet terminal, apersonal computer, or the like in which an interaction application isinstalled. The operation system 3 is a computer system used forperforming registration, update, or reference of operation data, such asa transportation expenses settlement system, an attendance managementsystem, a schedule management system, a facility reservation system, orthe like. Further, the external system 4 is a computer system providedby a business entity that is different from the operation system 3, anda route search system is illustrated as an example in the presentexample embodiment. The employee management system 5 may be a userinformation management system, a human resource information managementsystem, a general affairs information management system, or the like.

FIG. 2 is a diagram illustrating a specific example of an entry windowof the operation system 3 in the first example embodiment. In thisexample, an application content entry window is illustrated by which theuser sets input items such as “date of use”, “destination”, “section ofroute”, “one-way fare”, “one-way/round-trip category”, “dailyallowance”, “application category”, or the like, respectively, whenapplying transportation expenses at a business trip. The informationprocessing apparatus 1 according to the present example embodimentautomatically performs operation on the operation system 3 asillustrated in FIG. 2 instead of the user. The configuration ofinformation processing apparatus 1 will be described below in detail.

As illustrated in FIG. 1, the information processing apparatus 1 has anacceptance unit 10, a specifying unit 11, an interaction setting storageunit 12, a presentation unit 13, an extraction unit 14, a user answerstorage unit 15, a learning unit 16, a learning model storage unit 17,an RPA execution unit 18, an RPA setting storage unit 19, and a scriptstorage unit 20.

The acceptance unit 10 accepts a process request to the operation system3 input from the user terminal 2. In the present example embodiment, theprocess request is referred to as “task-specifying passage”. Forexample, a sentence including a purpose that the user wants to achievein the operation system 3 including various systems, such as “I want tohave my travel expenses settled”, “settlement of train fare”, “change ofdelivery date and time”, “reservation of a meeting room”, or the likecorresponds to a task-specifying passage (task declaration passage).Note that the acceptance unit 10 can support input of both a taskachievement-type interaction and a task non-achievement-typeinteraction. The task achievement-type interaction is an interactiveform in which a passage to specify a task is input first and a questionand an answer are repeated alternatingly. An example may be aninteraction in which a task-specifying passage of “what is the weatherlike now?” is input from the user terminal 2, and in response, aquestion of “which location of the weather are you talking about?” isprovided from the information processing apparatus 1, and “Minato wardin Tokyo” is input again from the user terminal 2. Since the task can bespecified from the initial input, there is an advantage of topics beingnarrowed before making a question. In contrast, the tasknon-achievement-type interaction is an interactive form in which atask-specifying passage and an answer are provided together. An examplemay be an interactive form such as “what is the weather like tomorrow inMinato ward?”. According to such an interactive form, when the userprovides information required for a process at once, a quick process isenabled without repetition of questions.

The specifying unit 11 references storage information in the interactionsetting storage unit 12, performs text analysis on a task-specifyingpassage input from the acceptance unit 10, and specifies an operationtask to be executed in the operation system 3. Herein, the “textanalysis” is a generic term for technologies of analyzing textinformation input or converted by using morphological analysis, syntaxanalysis (parsing), synonym term extraction, span extraction,implication recognition, or the like. The “morphological analysis” is aprocess of sectioning a sentence into morphemes, each of which is theminimum unit having a meaning as a language, and applying informationsuch as a base form, a part of speech, or the like thereto.

The “syntax analysis” is referred to as dependency analysis and is aprocess of calculating naturalness as dependency between a word and aclause, provided that a predetermined structural constriction issatisfied, and determining the dependency between the word and theclause.

The “synonym term extraction” is a process of inputting text informationto be processed and extracting a pair of synonym terms having the samemeaning but different written forms. For example, synonym terms may beextracted and stored for each particular domain (field) such as anIT-related domain, a machine-related domain, a cooking-related domain,or the like.

The “span extraction” is a process of using a model learned fromlearning data and automatically cutting and extracting an importantsection from the input sentence. An exemplary scheme of the spanextraction may be Conditional Random Field (CRF). For example, a casewhere three passages “I am going to travel to Hawaii with my family”, “Iam going to travel to America next month”, and “destination is New York”are input as learning data will be described. In such a case, withlearning of the learning data, it is determined that the word after“travel” and “to” is highly likely to be the destination. As a result,when the passage “I am going to travel to Italy” is input as unknowndata, “Italy” can be extracted as the destination. Further, the“implication recognition” is a process of, for two passages (sentences),determining whether or not one of the sentences (target sentence)includes the meaning represented by the other sentence (hypothesissentence).

Further, the specifying unit 11 references a model file when performingtext analysis. The model file is a model created by machine learning.The learning unit 16 described later collects a large number of correctpairs of a natural language and an answer item in order to extract ananswer item from a natural language in the extraction unit 14 andperforms machine learning by using the data (training data). Thereby,the learning unit 16 generates a model file so as to be able to extractan answer item from an input text. The interaction setting storage unit12 stores a task setting that defines an execution condition of anoperation task and an input condition setting that defines an inputcondition of an input item required at execution of the operation task.

FIG. 3A and FIG. 3B are diagrams illustrating an example of data itemsin the interaction setting storage unit 12 in the first exampleembodiment. In FIG. 3A, there are items of an operation task ID uniqueto each operation task, a start condition and an interruption conditionof the operation task, an input item ID of an input item required forexecution of the operation task, and a process at the end of theoperation task, and data examples for respective items are illustrated.The order of input item IDs indicates a question order. For example,when a task-specifying passage of “settlement of train fare” is input,the specifying unit 11 performs text analysis based on a model file(“Model_StartCon”) defining the start condition and specifies anoperation task ID (“Apply_TransExp”). Further, the input items requiredwhen the above task is executed are a date of use (“Date_use”), adeparture station (“From_station”), a destination station(“To_station”), a via station (“Via_station”), and a one-way/round-tripcategory (“kbn_way”). Further, it is indicated that a script“Script_001” is executed as the ending process.

In FIG. 3B, there are item examples of an input item ID, a questionpassage corresponding to the input item, a data type of the input item,an answer determination condition, and an external system cooperation,and data examples for respective items are illustrated. For example, itis indicated that, when the input item ID is “Date_use”, “When is thedate of use?” is presented as a question passage, the data type of setdata is “date type”, and a model file “Model_Date” is used as the answerdetermination condition.

Further, it is indicated in a case of the input item ID “kbn_way” that aquestion passage in which a route search result from the external systemcooperation and a sentence of “Is it a round-trip?” are combined ispresented and that the data type is “Yes/No type” and a program calledas the external system cooperation is “Search_Route” of the route searchsystem.

Note that, although a model file is used as the start condition, aninterruption condition, and an answer determination condition of anoperation task in FIG. 3A and FIG. 3B, the example embodiment is notlimited to the model determination form. For example, a regularexpression form such as “*change of date and time*”, “*quit*”,“xxxx-xxxx-xxx [0-9]+”, or the like may be used together. Further,another data type in the input condition setting may include a characterstring type, a numerical value type, or a model type.

The presentation unit 13 requests input items required at execution ofan operation task and presents a question passage related to the inputitem to the user terminal 2 side. The number of input items and questionpassages is different for operation tasks. The acceptance unit 10accepts an answer passage input at the user terminal 2 in response tothe presented question passage.

The extraction unit 14 performs text analysis on the answer passageaccepted by the acceptance unit 10 and extracts an answer itemcorresponding to the input item from the answer passages. The useranswer storage unit 15 stores an answer item extracted by the extractionunit 14 as a setting value. FIG. 4 is a diagram illustrating an exampleof data item of the user answer storage unit 15 in the first exampleembodiment. In this example, “2017/11/16” is stored for the date of use(“Date_use”), “Mita” is stored for the departure station(“From_station”), “Otemachi” is stored for the destination station(“To_station”), “216” is stored for the one-way fare (“fare”), “1(one-way)” is stored for the one-way/round-trip category (“kbn_way”) assetting values, respectively.

The learning unit 16 stores a pair of an input answer passage and ananswer item extracted from the answer passage in the user answer storageunit 15 as learning data. Further, when the extraction unit 14 correctlyextracts an answer item from an answer passage, the learning unit 16creates a learning model based on learning data and stores the createdlearning model in the learning model storage unit 17. The extractionunit 14 uses a learning model corresponding to an input item for textanalysis.

The RPA execution unit 18 sets an answer item as an argument to load ascript corresponding to an operation task from the script storage unit20 and execute the operation task. Herein, “Robotic Process Automation(RPA)” is software that operates various applications on behalf of aperson in back-office operations such as accounting, general affairs, orthe like, for example, and is referred to as a virtual intellectuallabor (Digital Labor). That is, the RPA is software used for operatingsoftware and can automate a typical operation such as manual entry ofdata.

FIG. 5 is a diagram illustrating an example of data item of the RPAsetting storage unit 19 in the first example embodiment. In thisexample, with respect to the RPA setting storage unit 19, an operationtask ID and an argument used for executing a script are the settingitems, and examples of data thereof are indicated.

FIG. 6 is a block diagram illustrating a hardware configuration exampleof the information processing apparatus 1 according to the first exampleembodiment. The information processing apparatus 1 has a centralprocessing unit (CPU) 101, a memory 102, a storage device 103, acommunication interface 104, an input device 105, and a display device106.

The CPU 101 is a processor that loads and executes a program stored inthe storage device 103 on the memory 102 and thereby performs overallcontrol and calculation processing of the information processingapparatus 1. Further, the CPU 101 stores data of a process result in thestorage device 103 and externally transmits the data of the processresult via the communication interface 104.

The memory 102 includes a random access memory (RAM) or the like thattemporarily stores data being processed by the CPU 101 or data read fromthe storage device 103.

The storage device 103 stores a program to be executed by the CPU 101,data that is a result of a process performed by a program, or the like.The storage device 103 includes a read only memory (ROM) dedicated toreading or a hard disk drive, a flash memory, or the like that arereadable and writable. Further, the storage device 103 may include acomputer readable portable storage medium such as a CD-ROM.

The communication interface 104 is a communication unit that transmitsand receives data and is configured to be able to perform acommunication scheme of at least one of wired communication and wirelesscommunication. The communication interface 104 includes a processor, anelectrical circuit, an antenna, a connection terminal, or the likerequired for the above communication scheme. The communication interface104 performs communication using the above communication scheme inaccordance with a signal from the CPU 101.

The input device 105 includes a keyboard or the like that accepts inputfrom the user and transmits the input content to the CPU 101 as asignal. A touchscreen in which the input device 105 and the displaydevice 106 are integrated may be used.

The display device 106 is a display device that displays predeterminedinformation in accordance with a signal from the CPU 101. As the displaydevice 106, any display device such as a liquid crystal display may beused.

Note that the information processing apparatus 1 is not limited to thehardware configuration illustrated in FIG. 6 but may further includeanother device. The information processing apparatus 1 may be formed ofone or a plurality of devices or may be integrally configured withanother device. Further, the information processing apparatus 1 may beconnected to a separate apparatus, and at least a part of processingperformed by the information processing apparatus 1 in the presentexample embodiment may be performed by the another apparatus.

Subsequently, the operation of the information processing apparatus 1according to the first example embodiment will be described withreference to FIG. 7 to FIG. 10.

FIG. 7 is a sequence diagram illustrating an example of operationsbetween the information processing apparatus 1, the user terminal 2, andthe operation system 3 according to the first example embodiment. FIG. 8and FIG. 9 is a diagram illustrating an example of an interactive windowof the user terminal 2 in the first example embodiment. In this example,speech balloons from the right side of the window represent inputcontents from the user terminal 2, and speech balloons from the leftside of the window represent output contents (question passages) fromthe information processing apparatus 1.

First, once the user inputs a task-specifying passage at the userterminal 2 (step S101), the specifying unit 11 of the informationprocessing apparatus 1 performs text analysis on the task-specifyingpassage accepted by the acceptance unit 10 to specify an operation taskand outputs the operation task ID thereof to the presentation unit 13(step S102).

In FIG. 8, when a natural passage (task-specifying passage T1) of “Iwant to have yesterday's train expenses settled” is input at the userterminal 2, question passage Q11 is presented. It is indicated inquestion passage Q11 that, even when ambiguous input of “train expensessettled” is included in task-specifying passage T1, an operation task of“transportation expenses settlement” was able to be specified by textanalysis.

Next, the presentation unit 13 of the information processing apparatus 1acquires question passages and question order corresponding to an inputitem required in executing the operation task from the interactionsetting storage unit 12 based on the operation task ID (step S103).

Next, the extraction unit 14 of the information processing apparatus 1determines whether or not an answer item is included in thetask-specifying passage (step S104). Herein, if the extraction unit 14determines that an answer item is included in the task-specifyingpassage (step S104: YES), the extraction unit 14 stores the answer itemincluded in the task-specifying passage (step S105). In response, sinceno question is necessary for the answer item stored in step S105, thepresentation unit 13 changes the question order (step S106). Incontrast, the information processing apparatus 1 determines that noanswer item is included in the task-specifying passage (step S104: NO),the process proceeds to the process of step S107.

In question passage Q11 in FIG. 8, the word of “yesterday” included intask-specifying passage T1 is converted into “on November 16 (Thursday)”based on the current date (November 17 (Friday)). That is, it ispossible to support relative specification such as “yesterday”. Then, inresponse to the “date of use” being fixed to “November 16” due to inputof answer passage A11, a question about “date of use” is omitted in theinteraction illustrated in FIG. 8.

In step S107, the presentation unit 13 determines whether or not thequestion form related to a target input item corresponds to a fixedquestion. The “fixed question” refers to a fixed question passagedescribed in advance in the input condition setting. The determinationfor a fixed question is performed in accordance with conditionsdescribed in the input condition setting. Herein, if the presentationunit 13 determines that the question form corresponds to a fixedquestion (step S107: YES), the process proceeds to the process of stepS109. For example, this corresponds to a case of the input item“Date_use (date of use)” illustrated as an example in FIG. 3B.

In contrast, if the presentation unit 13 determines that the questionform does not correspond to a fixed question (step S107: NO), thepresentation unit 13 acquires a question passage created by execution ofthe external system cooperation process (step S108), and the processproceeds to the process of step S109. The question passage whosequestion form does not correspond to a fixed question means a questionpassage that varies in accordance with a process result in the externalsystem 4. For example, this corresponds to a case of the input item“kbn_way (one-way/round-trip category)” illustrated as an example inFIG. 3B. The external system cooperation process will be describedlater.

In step S109, in response to the presentation unit 13 outputting aquestion passage corresponding to the input item to the user terminal 2,the user terminal 2 displays the question passage on the screen (stepS110).

Next, in response to the user inputting an answer passage to thequestion passage at the user terminal 2 (step S111), the extraction unit14 performs text analysis on the answer passage accepted by theacceptance unit 10 and extracts an answer item indicated by the answerpassage (step S112). Note that text analysis may be unnecessary for theanswer passage to the Yes/No type question passage. Further, in a caseof voice input, there are various answer patterns such as “Yes”, “Yes,it is”, “That's right”, and the like as an answer meaning “Yes”. In sucha case, it is preferable to perform text analysis.

Next, the extraction unit 14 determines whether or not a plurality ofanswer items are extracted from the answer passage in step S112 (stepS113). Herein, if the extraction unit 14 determines that a plurality ofanswer items are extracted (step S113: YES), the process proceeds to theprocess of step S114. In contrast, if the extraction unit 14 determinesthat a plurality of answer items are not extracted (step S113: NO), theprocess proceeds to the process of step S120.

In FIG. 8, in response to question passage Q12 (“Where is your departurestation?”), answer passage A12 (“from Mita to Otemachi”) is input.Answer passage A12 includes not only the answer item to the input itembeing questioned (“departure station”) but also the answer item toanother input item (“destination station”). Further, it can be said thatthe parts of “Mita” and “Otemachi” are ambiguous input to representstation names. In such a case, it is preferable to extract “Mita” as“Mita station” and “Otemachi” as “Otemachi station” based on apredetermined regular expression scheme. In FIG. 8, the informationprocessing apparatus 1 presents question passage Q13 (“Fare from Mitastation to Otemachi station is 216 Yen by Toei Subway Mita line withoutchange of trains. Is it a round-trip?”) in response to answer passageA12.

In step S114, the extraction unit 14 determines whether or not thecorrespondence between the plurality of answer items extracted from theanswer passage and the input item is clear. Herein, if the extractionunit 14 determines that the correspondence between the answer items andthe input item is clear (step S114, YES), the process proceeds to theprocess of step S119.

Although answer passage A12 in FIG. 8 includes not only an answer itemto the input item being questioned (“departure station”) but also ananswer item to another input item (“destination station”), thecorrespondence between the answer items and the input item is clearafter text analysis is performed on the answer passage A12. Thus, “Mita”is classified as “departure station”, and “Otemachi” is classified as“destination station”, as illustrated in question passage Q13.

In contrast, if the extraction unit 14 determines that thecorrespondence between the answer items and the input item is unclear(step S114: NO), the presentation unit 13 outputs a confirmationquestion passage to the user terminal 2 (step S115). In response, theuser terminal 2 displays the question passage on the screen (step S116).

In FIG. 9, in response to question passage Q22 (“where is your departurestation?”), answer passage A22 (“From Mita, I got off at Oshiage andwent to Aoto”) is input. In answer passage A22, it is clear that“departure station” is “Mita station”. However, when the sentence suchas answer passage A22 has not been learned, “via station” and“destination station” are processed as being unclear in the informationprocessing apparatus 1. That is, both “Oshiage station” and “Aotostation” may be candidates of the “destination station”. Thus,confirmation question passage Q23 (“Is Aoto station the destinationstation?”) is presented to determine whether or not “Aoto station”should be classified as “destination station”. When a confirmationquestion passage is created, a matching answer item will be selected bya question. It is therefore preferable that confirmation questionpassages be presented with respect to answer items in descending orderof classification score indicating the correlation degree to the inputitem when the answer passage is classified based on a machine learningmodel. For example, in answer passage A22 described above, since theword “to” is followed immediately before “Aoto”, the classificationscore related to “destination station” is higher for “Aoto station” thanfor “Oshiage station”.

Next, in response to the user inputting the answer passage to thequestion passage at the user terminal 2 (step S117), the extraction unit14 performs text analysis on the answer passage and extracts an answeritem indicated by the answer passage. Thereby, the extraction unit 14clarifies the correspondence between the plurality of answer items andthe input item (step S118).

In FIG. 9, in response to the user answering “Yes” (A23) to questionpassage Q23, “Aoto station” is determined as an answer item to“destination station”. Further, since the answer item of “via station”is blank, “Oshiage station” excluded by the previous answer (A23) isautomatically allocated as an answer item of “via station”. As a result,as indicated in the last question passage Q24 (“From Mita station viaOshiage station to Aoto station . . . ”), the correspondence betweenthree input items (“departure station”, “via station”, and “destinationstation”) and the answer item is clarified.

Next, the presentation unit 13 changes the question order so as to omita question related to the answer item whose correspondence to the inputitem has been clarified (step S119). In the example of a task settingillustrated in FIG. 3A, a question about “destination station(To_station)” is made after “departure station (From_station)” for anoperation task of transportation expenses application. In the example ofthe interaction in FIG. 8, however, a question about “destinationstation” is omitted because the question has been answered in answerpassage A12. Further, “via station” is omitted because there is no viastation (transfer station) on the searched route.

Next, in response to storing the answer item corresponding to the inputitem in the user answer storage unit 15 (step S120), the extraction unit14 determines whether or not questions about all the input itemsrequired to execute the operation task were finished (step S121).Herein, if the extraction unit 14 determines that questions about allthe input items were finished (step S121: YES), the process proceeds tothe process of step S122. In contrast, if the extraction unit 14determines that questions about all the input items were not finished(step S121: NO), the process returns to the process of step S107.

In step S122, the RPA execution unit 18 reads a script related to theoperation task from the script storage unit 20 and executes the scriptby using the answer items read from the user answer storage unit 15 asarguments.

In step S123, the operation system 3 performs an automatic process basedon the script and outputs the process result thereof to the informationprocessing apparatus 1 (step S124).

FIG. 10 is a flowchart illustrating one example of an external systemcooperation process in the information processing apparatus 1 accordingto the first example embodiment. This process is performed when a scriptand an argument for the external system cooperation are specified as aquestion form of a question passage related to an input item in an inputcondition setting of the interaction setting storage unit 12.

First, the RPA execution unit 18 reads a script related to an operationtask from the script storage unit 20, acquires an answer item to be setas an argument from the user answer storage unit 15, and executes thescript (step S201).

Next, the RPA execution unit 18 acquires a process result based on thescript from the external system 4 (step S202) and outputs the processresult to the presentation unit 13.

The presentation unit 13 then creates a question passage related to aninput item based on the process result (step S203). The created questionpassage is output to the user terminal 2 (step S109 of FIG. 7).

The part “is 216 Yen by Toei Subway Mita line without change of trains”in question passage Q13 in FIG. 8 described above is based on a processresult (a train line and a one-way fare) obtained from a route searchsystem (not illustrated) by using the answer items of “departurestation” and “destination station” extracted from answer passage A12 asarguments to execute the script.

As described above, according to the information processing apparatus 1of the present example embodiment, it is possible to construe themeaning and content of a sentence input by a natural language andautomatically perform a desired operation task without registering alarge number of rules in advance.

Further, a plurality of operation systems 3 are introduced in manycompanies, and the operation methods thereof are often different forrespective operation systems 3. In such a case, it takes time for a userto be familiar with the operation method of the system. In contrast,according to the information processing apparatus 1 of the presentexample embodiment, by a user simply answering in a natural language toone or more questions inquired via a common interaction application, itis possible to accept a process request to a desired operation system 3and input/specification of data and automatically process the requestedprocess instead of the user. Therefore, the user is no longer requiredto learn the operation method of the plurality of operation systems 3.As a result, this makes operations efficient.

Further, the script storage unit 20 that stores an RPA script used forperforming a predetermined process on the operation system 3 for eachoperation task is provided, and the RPA execution unit 18 loads an RPAscript corresponding to the operation task and executes the operationtask based on the RPA script and an answer item (argument). Because ofthe configuration using a script, there is an advantage of easyimplementation to the plurality of operation systems 3.

Further, when the extraction unit 14 extracts another answer itemcorresponding to another input item in addition to an answer itemcorresponding to an input item in an answer passage, the presentationunit 13 omits presentation of a question passage related to such anotherinput item. Since this eliminates a need for a re-question when ananswer item related to an unquestioned input item is obtained at thesame time, it is possible to acquire an answer item efficiently in aninteraction.

Further, when the extraction unit 14 extracts a plurality of answeritems for a common input item from an answer passage, the presentationunit 13 presents a question passage to confirm an answer itemcorresponding to the input item. Accordingly, even when answer itemscorresponding to the input item for the first question are not refinedto one answer item, it is possible to efficiently make a re-question toclarify the correspondence between the input item and the answer item.

In general, although the accuracy of a learning model obtained bymachine learning can be increased by repeating learning, the accuracymay be low in an initial phase of learning. In the present exampleembodiment, however, by making a re-question based on the order ofclassification scores, an answer item to an input item can be extractedat high accuracy even when an answer passage such as “From Mita, I gotoff at Oshiage and went to Aoto” described above is input for the firsttime.

Furthermore, the user answer storage unit 15 that stores an answer itemextracted from an answer passage and an answer passage of an extractionsource is further provided. Accordingly, even when answer itemscorresponding to an input item are not refined to one answer item from acertain answer passage, the correspondence clarified by the re-questionis stored as learning data (training data) and used in machine learning,and thereby an answer item can be extracted at high accuracy when asimilar answer passage is input.

Second Example Embodiment

The information processing apparatus 1 according to a second exampleembodiment will be described below. Note that references common to thereferences provided in the drawings of the first example embodimentrepresent the same objects. Thus, features different from those of thefirst example embodiment will be described in detail.

FIG. 11 is a block diagram illustrating the function of the informationprocessing apparatus 1 according to the second example embodiment. Theinformation processing apparatus 1 according to the second exampleembodiment further has a monitoring unit 21. The monitoring unit 21monitors registered data in the plurality of operation systems 3. Themonitoring unit 21 then specifies an operation task related toadjustment of data when data associated with an input item areinconsistent among the plurality of operation systems 3 or in apredetermined case (for example, when data is associated with particularevent information). When the monitoring unit 21 detects an inconsistencyin data, the presentation unit 13 presents a message (task recommendpassage) that proposes execution of a new operation task specified bythe monitoring unit 21.

Further, the extraction unit 14 acquires user information on the userfrom the employee management system 5 (user information managementsystem) and extracts an answer item corresponding to an input item fromthe user information. The extraction unit 14 then stores the extractedanswer item in the user answer storage unit 15. The presentation unit 13omits presentation of a question passage related to an input itemoverlapping the user information when using the user information.

The operation of the information processing apparatus 1 according to thesecond example embodiment will be described below based on FIG. 12 toFIG. 14. FIG. 12 is a flowchart illustrating one example of theinformation processing apparatus 1 in the second example embodiment.Further, each of FIG. 13 and FIG. 14 is a diagram illustrating oneexample of an interactive window of the user terminal 2 in the secondexample embodiment.

First, the monitoring unit 21 determines whether or not a predeterminedstartup event is detected (step S301). Herein, if the monitoring unit 21detects a startup event (step S301: YES), the monitoring unit 21analyzes registered data related to the same user among the plurality ofoperation systems 3 (step S302). The startup event may be startup of theuser terminal 2 being monitored or a registration/update event of datafrom the user terminal 2 to the operation system 3.

In step S303, the monitoring unit 21 determines whether or not there areinconsistent data among the plurality of operation systems 3. Herein, ifthe monitoring unit 21 determines that there are inconsistent data, themonitoring unit 21 specifies an operation task used for adjusting dataand notifies the presentation unit 13 of the specified operation taskand an input item to be adjusted (step S304).

The recommend passage TR3 (“Good morning, Mr. Nichiden. I would like toconfirm the meeting at company A approved just now.”) of the operationtask illustrated in FIG. 13 is a sentence presented in response to aschedule being registered in a schedule management system, which is oneof the operation systems 3, as a trigger from the user terminal 2. Inthis example, although the case where the user (Taro Nichiden) registersa schedule by himself is illustrated, there may be a case where acoworker (supervisor) of the user registers a schedule for the user. Themonitoring unit 21 detects that a schedule after the end of meeting isnot registered in the schedule management system and thereby notifiesthe presentation unit 13 of an input item to be adjusted.

Next, the presentation unit 13 acquires an input item and a questionpassage related to the operation task (step S305), selects a questionpassage of the input item to be adjusted (step S306), and in response,outputs the question passage to the user terminal 2 (step S307).

Next, in response to acquiring an answer passage input to the questionpassage at the user terminal 2, the extraction unit 14 extracts ananswer item from the answer passage by performing text analysis on theanswer passage (step S308). The extraction unit 14 stores the extractedanswer item in the user answer storage unit 15 (step S309).

Next, the extraction unit 14 determines whether or not the question wasfinished for all the input items required for execution of the operationtask (step S310). Herein, if the extraction unit 14 determines that thequestion was finished for all the input items (step S310: YES), theprocess proceeds to the process of step S311. In contrast, if theextraction unit 14 determines that the question was not finished (stepS311: NO), the process returns to the process of step S306.

In step S311, the RPA execution unit 18 reads a script related to theoperation task from the script storage unit 20. The RPA execution unit18 then executes the script by using a setting value of an answer itemread from the user answer storage unit 15 as an argument.

In the example of FIG. 13, Yes/No type question passage Q31 (“Will youreturn to the office after the meeting?”) is presented, and answerpassage A31 (“Yes, I will”) is input. The information processingapparatus 1 presents the message M32 (“Thank you. So, temporarytraveling time will be added to the scheduler.”) to the user terminal 2in response to the end of the question. The message M32 indicates that ascript related to the operation task of schedule registration isexecuted to register the temporary schedule in the schedule managementsystem.

Subsequently, an example of an interaction in FIG. 14 will be described.This interaction is started up when the information processing apparatus1 detects startup of the user terminal 2 on the day after the meeting.First, recommend passage TR4 of an operation task (“Good morning, Mr.Nichiden. I would like to make sure the yesterday's transportation routeand fare. Is it OK?”) is presented. This indicates that, since the placeand the time range of the meeting have been registered in the schedulemanagement system, transportation expenses application is specified asan operation task necessary for the user.

Question passage Q41 (“There was no record of entry to the company afterthe meeting, yesterday. Was the schedule changed?”) is a questionindicating that an inconsistency in data is detected between theschedule management system (scheduler) and an entry/exit managementsystem. An operation task of transportation expenses application isspecified as a parent task due to answer A40 responding to recommendpassage TR4. Further, it is indicated that an operation task ofreschedule can be specified and presented as a child task in associationwith the parent task.

In response to answer passage A41 to question passage Q41 (“The meetingended an hour later than scheduled, so I went straight home”) beinginput, message M42 (“So, I will adjust the scheduler to register “wentstraight home” as the yesterday's end of work in the attendancemanagement system.”) is presented. Message M42 indicates that answerpassage A41 is analyzed and data adjustment is automatically performedfor both the schedule management system and the attendance managementsystem.

In question passage Q43 (“Is the return transportation route fromcompany A as follow?”), a search result of the transportation routebetween the company A and the user's home is presented. Thetransportation route is acquired by using “departure station” and“destination station” as arguments to request a search result from theroute search system, which is the external system 4.

In the example of the interaction illustrated in FIG. 14, a case isillustrated where a place of the meeting (“Otemachi”) is extracted fromregistered data in the schedule management system, for example, for“departure station”, and the nearest station of the user's home(“Musashi-Kosugi”) is extracted from the user information managementsystem, for example, for “destination station”, which are automaticallyset.

That is, the information processing apparatus 1 acquires systempossession information associated with the user from the userinformation management system and stores the acquired system possessioninformation in the user answer storage unit 15 in association with apredetermined answer item in advance. In acquisition, an employee numberis used as a key, for example. Note that the system from which data isacquired is not limited to the user information management system. Asspecific examples, “home nearest station”, “company nearest station”,“approver information”, “census register name”, “position”, “homeaddress information”, or the like may be acquired as information uniqueto the user from the personal affairs information management system.Similarly, when there is a system that manages information on a customercompany, company information, address information, or the like onanother company can be acquired. The acquired information can beutilized when being the input item at execution of an operation task inthe operation system 3 including various systems.

As described above, according to the information processing apparatus 1of the second example embodiment, by monitoring data accumulated in theoperation systems 3, it is possible to estimate and present an operationtask required for the user when an inconsistency (discrepancy orcontradiction) in data is detected for an input item having associationbetween different operation systems 3. Accordingly, it is possible tocorrect the inconsistency in data eventually. In the example describedabove, data is consistent between both systems of the schedulemanagement system and the transportation expenses application system.Further, since information required for execution of an operation taskcan be automatically extracted from the operation system 3 side andutilized, the number of questions presented to the user terminal 2 maybe reduced, and an operation task may be efficiently executed.

Further, the monitoring unit 21 recommends an operation task not onlywhen data are inconsistent between systems but also when data areassociated with particular event information. For example, when data ofa company name such as “company A” or a place name such as “Otemachi” isregistered as a destination of a business trip in the schedulemanagement system and when a meeting room name such as “meeting room234” is in a facility reservation system, an event of a meeting outsidethe company is detected, and required data are adjusted/registered.

Third Example Embodiment

The operation of the information processing apparatus 1 according to athird example embodiment will be described below based on FIG. 15 andFIG. 16. FIG. 15 is a flowchart illustrating one example of theinformation processing apparatus 1 in the third example embodiment. FIG.16 is a diagram illustrating one example of an interactive window of theuser terminal 2 in the third example embodiment. Note that the mark of amicrophone illustrated in FIG. 16 indicates that each answer is input byvoice input.

The extraction unit 14 of the information processing apparatus 1analyzes an answer passage input at the user terminal 2 (step S401) anddetermines whether or not a correction expression is included in theanswer passage (step S402). Herein, if the extraction unit 14 determinesthat a correction expression is included (step S402: YES), theextraction unit 14 specifies an answer item to be corrected from theanswer passage (step S403) and extracts a corrected answer item (stepS404). A method of detecting a correction expression such as “No, Imean”, “was a mistake”, or the like from an answer passage may be amethod of detection in a rule base based on a regular expression or amethod of using machine learning. Note that similar processing may beapplied not only when correction is immediately expressed in a singleanswer passage but also when correction is expressed afterward for ananswered input item in an interaction related to the same operationtask.

In contrast, if the extraction unit 14 determines that no correctionpassage is included in the answer passage (step S402: NO), theextraction unit 14 extracts an answer item corresponding to an inputitem from the answer passage (step S405). In step S406, the extractionunit 14 stores the extracted answer item in the user answer storage unit15.

In the example of the interaction illustrated in FIG. 16, an operationtask of transportation expenses application is specified and a pluralityof question passages corresponding to the input item are presented, andanswer passage A52 (“From Mita to Musashi-Kosugi. No, I mean, from Mitato Otemachi”) input by voice includes a correction expression(“Musashi-Kosugi. No, I mean, from Mita to Otemachi”) to correct“destination station” from “Musashi-Kosugi” to “Otemachi”. Note that itis considered that, when the content of the answer passage is “From Mitato Musashi-Kosugi. No, I mean, Otemachi”, the information processingapparatus 1 may be unable to determine whether “Otemachi” is an item forcorrecting “Mita” or “Musashi-Kosugi”. When the content of the answerpassage can be construed in multiple meanings, a re-question such as“From Mita station to Otemachi station?” is made, for example.

The next question passage Q53 (“Fare from Mita station to Otemachistation is 216 Yen by Toei Subway Mita line without change of trains. Isit a round-trip?”) in response to answer passage A52 indicates that“Otemachi station” is extracted as an answer item of “destinationstation” from the corrected passage and route search was performed.

According to the information processing apparatus 1 of the third exampleembodiment, correction can be easily performed for an answer itemanswered in an interaction. In particular, this is effective wheninteraction processing is performed via voice between the user terminal2 and the information processing apparatus 1, because the answeredcontent cannot be deleted and therefore restatement would otherwise benecessary.

Fourth Example Embodiment

The operation of the information processing apparatus 1 according to afourth example embodiment will be described below based on FIG. 17 andFIG. 18. The present example embodiment is different from the exampleembodiments described above in that information acquired in an operationtask executed earlier is utilized in an operation task executed later.FIG. 17 is a flowchart illustrating one example of the informationprocessing apparatus 1 in the fourth example embodiment. In thisexample, a case where the operation system 3 is a delivery managementsystem is illustrated.

First, the specifying unit 11 of the information processing apparatus 1specifies an operation task from a task-specifying passage input fromthe user terminal (step S501) and acquires a scenario ID into which anoperation task ID is classified (step S502).

Next, the extraction unit 14 references the interaction setting storageunit 12 and searches a common input item (hereafter, referred to as“common input item”) from the operation task associated with the samescenario ID (step S503).

Next, the extraction unit 14 references the user answer storage unit 15based on the input item ID of the common input item and determineswhether or not the answer item for the common input item has alreadybeen registered/stored (step S504). Herein, if the extraction unit 14determines that the answer item to the common input item has alreadybeen stored (step S504: YES), the extraction unit 14 acquires data ofthe answer item for the common input item from the user answer storageunit 15 (step S505) and changes the order of questions required for theoperation task specified in step S501 (step S506). That is, since theanswer item has been fixed for the common input item, the question isomitted.

Next, the information processing apparatus 1 extracts an answer itemcorresponding to an input item required for execution of the operationtask in an interactive form and executes the operation task (step S507).

Then, upon completion of the execution of the operation task for theoperation system 3, the answer item corresponding to the common inputitem and the time when the answer item is set are stored in associationwith the scenario ID (step S508), and the process ends.

FIG. 18 is a diagram illustrating one example of an interactive windowof the user terminal 2 in the fourth example embodiment. Note thatdescription will be provided under the assumption that operation tasksof confirmation of delivery date and time and change of delivery dateand time are classified into the same scenario ID and “slip number” isthe common input item to the two operation tasks.

In FIG. 18, first, task-specifying passage T6 (“I want to confirmdelivery date and time”) is input from the user, an operation task ofconfirmation of delivery date and time is specified, and therebyquestion passage Q61 (“Please enter slip number.”) related toconfirmation of delivery date and time is presented.

In response to the user entering answer passage A61 including a slipnumber to question passage Q61, the information processing apparatus 1presents message M62 (“Delivery is scheduled to today, October 31(Tuesday) at 19:00 to 21:00.”) and completes the operation task ofconfirmation of delivery date and time.

Subsequently, new task-specifying passage T7 (“I want to change deliverydate and time”) is input after some period of time in a day of executionof the operation task of confirmation of delivery date and time. Theoperation task of confirmation of delivery date and time is specified inresponse to task-specifying passage T7, and thereby question passage Q63(“What delivery date and time would you like?”) related to change ofdelivery date and time is presented. A question passage of “Please enterslip number.” is supposed to be initially presented because newtask-specifying passage T7 is input, however, since the operation taskof confirmation of delivery date and time was executed on the same dayand the slip number has already been input, entry of a slip number isomitted.

Then, answer passage A63 (“Tomorrow, at 19:00”) to question passage Q63is analyzed, and thereby the last question passage Q64 (“Delivery dateand time will be changed to November 1 (Wednesday) at 19:00 to 21:00, isit OK?”) is presented.

According to the information processing apparatus of the fourth exampleembodiment, when a separate operation task classified into the samescenario ID has already been executed, a setting value of an answer itemcorresponding to a common input item can be reused at execution of a newoperation task. Thus, when a new task-specifying passage is input, sinceit is no longer necessary to present a question passage related to thecommon input item, it is possible to execute an associated operationtask continuously and thus effectively.

Fifth Example Embodiment

FIG. 19 is a block diagram illustrating the function of the informationprocessing apparatus 1 in a fifth example embodiment. Herein, theacceptance unit 10 has a text recognition unit 10A, a voice recognitionunit 10B, and a voice generation unit 10C. Further, the user terminal 2has a microphone 2C used for voice input and a speaker 2D used for voiceoutput in addition to a chat application 2A and an audio application 2B.Note that the microphone 2C and the speaker 2D may be an integrated typeas a hearable device (wireless earphone).

The text recognition unit 10A of the acceptance unit 10 recognizes textinformation transmitted from the chat application 2A of the userterminal 2 and outputs the text information to the specifying unit 11and the extraction unit 14. The voice recognition unit 10B recognizesvoice input from the microphone 2C of the user terminal 2, converts thevoice into text information, and outputs the text information to thespecifying unit 11 and the extraction unit 14. The processes in thespecifying unit 11 and the extraction unit 14 are the same as thoseperformed when text information is input from the text recognition unit10A. The voice generation unit 10C generates an audio file from textinformation of a question passage when determining the question passagecorresponding to an operation task based on a task-specifying passage inthe presentation unit 13. The voice generation unit 10C then outputs theaudio file to the user terminal 2 via the presentation unit 13. Inresponse, at the user terminal 2, voice of the audio file is output fromthe speaker 2D via the audio application 2B.

That is, in the present example embodiment, entry of a task-specifyingpassage, presentation of a question, and entry of an answer can beperformed via voice between the user terminal 2 and the informationprocessing apparatus 1. According to the present example embodiment, itis no longer necessary to manually enter text information on the userterminal 2, and it is possible to more easily execute an operation task.

Sixth Example Embodiment

FIG. 20 is a block diagram illustrating the function of an informationprocessing apparatus 7 according to a sixth example embodiment. Theinformation processing apparatus 7 according to the sixth exampleembodiment includes an acceptance unit 71 that accepts a process requestto an operation system, a specifying unit 72 that, based on the processrequest, specifies an operation task to be executed in the operationsystem, an extraction unit 73 that performs text analysis on the processrequest and extracts an answer item corresponding to an input itemrequired at execution of the operation task from the process request,and an execution unit 74 that executes the operation task based on theanswer item.

According to the information processing apparatus 7 of the sixth exampleembodiment, it is possible to accurately analyze an input naturallanguage and automatically set an input item required in the operationof an operation system without requiring pre-registration of a largenumber of rules.

Modified Example Embodiment

While the present invention has been described above with reference tothe example embodiments, the present invention is not limited to theexample embodiments described above. Various modifications that may beunderstood by those skilled in the art can be made to the configurationand details of the present invention within the scope not departing fromthe spirit of the present invention. For example, it should beunderstood that an example embodiment in which a part of theconfiguration of any of the example embodiments is added to anotherexample embodiment or an example embodiment in which a part of theconfiguration of any of the example embodiments is replaced with a partof the configuration of another example embodiment is also one of theexample embodiments to which the present invention may be applied.

For example, while the case where the operation system 3 that executesan operation task is a transportation expenses application system or adelivery management system has been described in the above exampleembodiments, targeted operation systems 3 are not limited thereto. Someuse cases will be described below.

(1) In a case of a facility reservation system, an operation task may besearch for an available facility such as a meeting room, reservation ofa facility, confirmation of a reservation, cancellation of areservation, or the like. By specifying an operation task with aninteraction process via texts or voices and executing the specifiedoperation task, it is possible to improve usage efficiency of afacility.(2) In a case of an attendance management system, an operation task maybe confirmation of a work record, admission of a work record,application of leave, approval of application of leave, or the likeperformed by the user of interest or a supervisor of the user.(3) In a case of a building entry application system, an operation taskmay be application of entry to a building, cancellation of applicationof entry to a building, change of application of entry to a building, orthe like. In each case of (1) to (3), it is not necessary for the userto directly access an entry window of the operation system 3 to manuallyset required input items, and it is possible to automatically set inputitems during an interaction and easily and efficiently execute anoperation task.

Further, the scope of each of the example embodiments includes aprocessing method that stores, in a storage medium, a program thatcauses the configuration of each of the example embodiments to operateso as to implement the function of each of the example embodimentsdescribed above, reads the program stored in the storage medium as acode, and executes the program in a computer. That is, the scope of eachof the example embodiments also includes a computer readable storagemedium. Further, each of the example embodiments includes not only thestorage medium in which the program described above is stored but alsothe program itself.

As the storage medium, for example, a floppy (registered trademark)disk, a hard disk, an optical disk, a magneto-optical disk, a compactdisc-read only memory (CD-ROM), a magnetic tape, a nonvolatile memorycard, or a ROM can be used. Further, the scope of each of the exampleembodiments includes an example that operates on operating system (OS)to perform a process in cooperation with another software or a functionof an add-in board without being limited to an example that performs aprocess by an individual program stored in the storage medium.

The whole or part of the example embodiments disclosed above can bedescribed as, but not limited to, the following supplementary notes.

(Supplementary Note 1)

An information processing apparatus comprising:

an acceptance unit that accepts a process request to an operationsystem;

a specifying unit that, based on the process request, specifies anoperation task to be executed in the operation system;

an extraction unit that performs text analysis on the process requestand extracts an answer item corresponding to an input item required atexecution of the operation task from the process request; and

an execution unit that executes the operation task based on the answeritem.

(Supplementary Note 2)

The information processing apparatus according to supplementary note 1,wherein the extraction unit performs an implication recognition processas the text analysis.

(Supplementary Note 3)

The information processing apparatus according to supplementary note 1or 2 further comprising a storage unit that stores, for each operationtask, a script used for causing the operation system to perform apredetermined process,

wherein the execution unit execute the operation task based on thescript corresponding to the operation task and on the answer item.

(Supplementary Note 4)

The information processing apparatus according to any one of claims 1 to3 further comprising a presentation unit that specifies the input itemcorresponding to the operation task specified by the specifying unit andpresents a question passage related to the input item,

wherein the acceptance unit accepts an answer passage to the questionpassage, and

wherein the extraction unit performs the text analysis on the answerpassage and extracts the answer item corresponding to the input itemfrom the answer passage.

(Supplementary Note 5)

The information processing apparatus according to supplementary note 4,wherein when the extraction unit extracts another answer itemcorresponding to another input item in addition to the answer itemcorresponding to the input item from the answer passage, thepresentation unit omits presentation of the question passage related tothe another input item.

(Supplementary Note 6)

The information processing apparatus according to supplementary note 4or 5, wherein when the extraction unit extracts a plurality of answeritems for a common input item from the answer passage, the presentationunit presents the question passage used for confirming the answer itemcorresponding to the input item.

(Supplementary Note 7)

The information processing apparatus according to supplementary note 6,wherein the presentation unit calculates respective classificationscores indicating correlation degrees between the input item and theanswer items and presents the question passage based on order of theclassification scores.

(Supplementary Note 8)

The information processing apparatus according to any one ofsupplementary notes 4 to 7 further comprising a user answer storage unitthat stores the answer item extracted from the answer passage and theanswer passage of an extraction source.

(Supplementary Note 9)

The information processing apparatus according to any one ofsupplementary notes 4 to 8,

wherein the extraction unit acquires system possession informationpossessed by the operation system in association with a user whoprovides the process request and extracts the answer item correspondingto the input item from the system possession information, and

wherein the presentation unit omits presentation of the question passagerelated to the input item overlapping the system possession information.

(Supplementary Note 10)

The information processing apparatus according to any one ofsupplementary notes 4 to 9 further comprising a monitoring unit that,when data associated with the input item are inconsistent among aplurality of operation systems or when the data are associated with apredetermined event information, specifies the operation task to adjustthe data,

wherein the presentation unit presents a message that proposes executionof the operation task specified by the monitoring unit.

(Supplementary Note 11)

The information processing apparatus according to any one ofsupplementary notes 4 to 10, wherein when a correction expression forthe answer item is included in the answer passage, the extraction unitspecifies an answer item to be corrected and a corrected answer itemfrom the answer passage and replaces the answer item to be correctedwith the corrected answer item.

(Supplementary Note 12)

An information processing method comprising: accepting a process requestto an operation system;

based on the process request, specifying an operation task to beexecuted in the operation system;

performing text analysis on the process request and extracting an answeritem corresponding to an input item required at execution of theoperation task from the process request; and

executing the operation task based on the answer item.

(Supplementary Note 13)

A storage medium storing a program that causes a computer to perform:

accepting a process request to an operation system;

based on the process request, specifying an operation task to beexecuted in the operation system;

performing text analysis on the process request and extracting an answeritem corresponding to an input item required at execution of theoperation task from the process request; and

executing the operation task based on the answer item.

REFERENCE SIGNS LIST

-   1, 7 information processing apparatus-   2 user terminal-   2A chat application-   2B audio application-   2C microphone-   2D speaker-   3 operation system-   4 external system-   5 employee management system-   10 acceptance unit-   10A text recognition unit-   10B voice recognition unit-   10C voice generation unit-   11 specifying unit-   12 interaction setting storage unit-   13 presentation unit-   14 extraction unit-   15 user answer storage unit-   16 learning unit-   17 learning model storage unit-   18 RPA execution unit-   19 RPA setting storage unit-   20 script storage unit-   71 acceptance unit-   72 specifying unit-   73 extraction unit-   74 execution unit-   101 CPU-   102 memory-   103 storage device-   104 communication interface-   105 input device-   106 display device

The invention claimed is:
 1. An information processing apparatuscomprising: at least one memory configured to store instructions; and atleast one processor configured to execute the instructions to: accept aprocess request for an operation system input from a user; perform textanalysis on the process request and specifies an operation task to beexecuted in the operation system; specify an input item required atexecution of the operation task and presents a question passage relatedto the input item to the user; perform text analysis on an answerpassage to the question passage input from the user and extracts ananswer item corresponding to the input item from the answer passage; andexecute the operation task and registering correspondence between theinput item and the answer item in the operation system.
 2. Theinformation processing apparatus according to claim 1, wherein the atleast one processor performs an implication recognition process as thetext analysis.
 3. The information processing apparatus according toclaim 1, wherein the at least one processor is further configured tostore, for each operation task, a script used for causing the operationsystem to perform a predetermined process, execute the operation taskbased on the script corresponding to the operation task and on theanswer item.
 4. The information processing apparatus according to claim1, wherein the at least one processor is configured to accept the answerpassage to the question passage.
 5. The information processing apparatusaccording to claim 4, wherein when the at least one processor extractsanother answer item corresponding to another input item in addition tothe answer item corresponding to the input item from the answer passage,the at least one processor omits presentation of the question passagerelated to the another input item.
 6. The information processingapparatus according to claim 4, wherein when the at least one processorextracts a plurality of answer items for a common input item from theanswer passage, the at least one processor presents the question passageused for confirming the answer item corresponding to the input item. 7.The information processing apparatus according to claim 6, wherein theat least one processor calculates respective classification scoresindicating correlation degrees between the input item and the answeritems and presents the question passage based on order of theclassification scores.
 8. The information processing apparatus accordingto claim 4, wherein the at least one processor is further configured tostore the answer item extracted from the answer passage and the answerpassage of an extraction source.
 9. The information processing apparatusaccording to claim 4, wherein the at least one processor acquires systempossession information possessed by the operation system in associationwith a user who provides the process request and extracts the answeritem corresponding to the input item from the system possessioninformation, and wherein the at least one processor omits presentationof the question passage related to the input item overlapping the systempossession information.
 10. The information processing apparatusaccording to claim 4, wherein the at least one processor is furtherconfigured to, when data associated with the input item are inconsistentamong a plurality of operation systems or when the data are associatedwith a predetermined event information, specifies the operation task toadjust the data; and present a message that proposes execution of thespecified operation task.
 11. The information processing apparatusaccording to claim 4, wherein when a correction expression for theanswer item is included in the answer passage, the at least oneprocessor specifies an answer item to be corrected and a correctedanswer item from the answer passage and replaces the answer item to becorrected with the corrected answer item.
 12. An information processingmethod comprising: accepting a process request for an operation systeminput from a user; performing text analysis on the process request andspecifying an operation task to be executed in the operation system;specifying an input item required at execution of the operation task andpresenting a question passage related to the input item to the user;performing text analysis on an answer passage to the question passageinput from the user; extracting an answer item corresponding to theinput item from the answer passage; and executing the operation task forregistering the correspondence between the input item and the answeritem in the operation system.
 13. A non-transitory storage mediumstoring a program that causes a computer to perform: accepting a processrequest for an operation system input from a user; performing textanalysis on the process request and specifying an operation task to beexecuted in the operation system; specifying an input item required atexecution of the operation task and presenting a question passagerelated to the input item to the user; performing text analysis on ananswer passage to the question passage input from the user; extractingan answer item corresponding to the input item from the answer passage;and executing the operation task for registering the correspondencebetween the input item and the answer item in the operation system.